AI/ML Development
Models trained on your own data, judged against a baseline, and handed over with the weights, the code and the runbook.
Scope a model ↗
What is custom machine learning development?
Custom machine learning development means training a model on your data to make one specific prediction — which invoices will be paid late, which parts are about to fail, which scans need a second look. It’s a different thing from prompting a general model: the value comes from patterns that only exist in your history, and nobody else can rent them.
The unglamorous truth is that most of the work is data, not modelling. Before anything is trained we agree the single metric the model will be judged on and build the dumbest possible baseline — a rule, an average, a lookup — because if a real model can’t beat that by a margin worth paying for, the honest answer is to keep the rule.
What a model build includes
Data and label audit
We check whether the data can actually support the prediction, and how the labels were made, before anyone promises an accuracy number.
A baseline to beat
A simple rule or statistical model shipped first, so every later result has something honest to be measured against.
Feature pipeline
The same transformations in training and in production, under version control, so the model doesn’t quietly see different data once it’s live.
Tracked experiments
Every run logged with its data snapshot, parameters and score, so a result from six weeks ago can be reproduced or challenged.
Inference service
The model behind a versioned API with a latency budget, and the ability to route a share of traffic back to the baseline.
Drift monitoring
Alerts on input distribution and outcome quality, plus a written retraining trigger so nobody has to guess when the model went stale.
How a model gets built
Frame the prediction
We turn the business question into one prediction, one metric and one threshold for being useful. If those can’t be written down, modelling is premature.
Interrogate the data
We profile what you have, hunt for leakage and gaps, and tell you early if the dataset simply can’t support the target.
Baseline, then model
The simple approach ships first. Candidates are then compared against it on held-out data you can inspect yourself.
Serve and shadow
The winning model goes behind an API and runs in shadow mode against live traffic before it influences a single real decision.
Hand over the loop
You get the weights, the training code, the retraining runbook and the monitoring — not a black box that only we can restart.
AI/ML Development FAQ
Most first models land between $15k and $60k, driven far more by the state of the data than by the algorithm. We quote the data work as its own line so you can see what you’re actually paying for.
The tools we build with
Reproducibility over novelty: every pick is one that lets us rerun an experiment from a year ago and swap the model without rewriting the service around it.
Modelling
Python
PyTorch
scikit-learn
Pandas
NumPyData & features
PostgreSQL
Apache Spark
Snowflake
DatabricksServing
FastAPI
Docker
Kubernetes
AWSTrack & watch
Weights & Biases
OpenTelemetry
Grafana
SentryRelated work
Related reading
Got a prediction worth making?
Tell us the decision you’d like to make better and what data sits behind it. We’ll come back with a baseline, a metric and a fixed first milestone.